Overview

Dataset statistics

Number of variables11
Number of observations20640
Missing cells207
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 MiB
Average record size in memory88.0 B

Variable types

Numeric10
Categorical1

Alerts

longitude is highly overall correlated with latitudeHigh correlation
latitude is highly overall correlated with longitudeHigh correlation
total_rooms is highly overall correlated with total_bedrooms and 2 other fieldsHigh correlation
total_bedrooms is highly overall correlated with total_rooms and 2 other fieldsHigh correlation
population is highly overall correlated with total_rooms and 2 other fieldsHigh correlation
households is highly overall correlated with total_rooms and 2 other fieldsHigh correlation
median_income is highly overall correlated with median_house_valueHigh correlation
median_house_value is highly overall correlated with median_incomeHigh correlation
total_bedrooms has 207 (1.0%) missing valuesMissing
df_index is uniformly distributedUniform
df_index has unique valuesUnique

Reproduction

Analysis started2023-08-23 04:36:49.085755
Analysis finished2023-08-23 04:37:03.267957
Duration14.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct20640
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10319.5
Minimum0
Maximum20639
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2023-08-23T12:37:03.382318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1031.95
Q15159.75
median10319.5
Q315479.25
95-th percentile19607.05
Maximum20639
Range20639
Interquartile range (IQR)10319.5

Descriptive statistics

Standard deviation5958.3991
Coefficient of variation (CV)0.57739223
Kurtosis-1.2
Mean10319.5
Median Absolute Deviation (MAD)5160
Skewness0
Sum2.1299448 × 108
Variance35502520
MonotonicityStrictly increasing
2023-08-23T12:37:03.549365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
13786 1
 
< 0.1%
13764 1
 
< 0.1%
13763 1
 
< 0.1%
13762 1
 
< 0.1%
13761 1
 
< 0.1%
13760 1
 
< 0.1%
13759 1
 
< 0.1%
13758 1
 
< 0.1%
13757 1
 
< 0.1%
Other values (20630) 20630
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
20639 1
< 0.1%
20638 1
< 0.1%
20637 1
< 0.1%
20636 1
< 0.1%
20635 1
< 0.1%
20634 1
< 0.1%
20633 1
< 0.1%
20632 1
< 0.1%
20631 1
< 0.1%
20630 1
< 0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct844
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.5697
Minimum-124.35
Maximum-114.31
Zeros0
Zeros (%)0.0%
Negative20640
Negative (%)100.0%
Memory size161.4 KiB
2023-08-23T12:37:03.718959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-124.35
5-th percentile-122.47
Q1-121.8
median-118.49
Q3-118.01
95-th percentile-117.08
Maximum-114.31
Range10.04
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.0035317
Coefficient of variation (CV)-0.016756182
Kurtosis-1.3301524
Mean-119.5697
Median Absolute Deviation (MAD)1.28
Skewness-0.29780121
Sum-2467918.7
Variance4.0141394
MonotonicityNot monotonic
2023-08-23T12:37:03.898947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.31 162
 
0.8%
-118.3 160
 
0.8%
-118.29 148
 
0.7%
-118.27 144
 
0.7%
-118.32 142
 
0.7%
-118.28 141
 
0.7%
-118.35 140
 
0.7%
-118.36 138
 
0.7%
-118.19 135
 
0.7%
-118.37 128
 
0.6%
Other values (834) 19202
93.0%
ValueCountFrequency (%)
-124.35 1
 
< 0.1%
-124.3 2
 
< 0.1%
-124.27 1
 
< 0.1%
-124.26 1
 
< 0.1%
-124.25 1
 
< 0.1%
-124.23 3
< 0.1%
-124.22 1
 
< 0.1%
-124.21 3
< 0.1%
-124.19 4
< 0.1%
-124.18 6
< 0.1%
ValueCountFrequency (%)
-114.31 1
 
< 0.1%
-114.47 1
 
< 0.1%
-114.49 1
 
< 0.1%
-114.55 1
 
< 0.1%
-114.56 1
 
< 0.1%
-114.57 3
< 0.1%
-114.58 2
< 0.1%
-114.59 2
< 0.1%
-114.6 3
< 0.1%
-114.61 3
< 0.1%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct862
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.631861
Minimum32.54
Maximum41.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2023-08-23T12:37:04.078871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32.54
5-th percentile32.82
Q133.93
median34.26
Q337.71
95-th percentile38.96
Maximum41.95
Range9.41
Interquartile range (IQR)3.78

Descriptive statistics

Standard deviation2.1359524
Coefficient of variation (CV)0.059945013
Kurtosis-1.1177598
Mean35.631861
Median Absolute Deviation (MAD)1.23
Skewness0.465953
Sum735441.62
Variance4.5622926
MonotonicityNot monotonic
2023-08-23T12:37:04.254132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.06 244
 
1.2%
34.05 236
 
1.1%
34.08 234
 
1.1%
34.07 231
 
1.1%
34.04 221
 
1.1%
34.09 212
 
1.0%
34.02 208
 
1.0%
34.1 203
 
1.0%
34.03 193
 
0.9%
33.93 181
 
0.9%
Other values (852) 18477
89.5%
ValueCountFrequency (%)
32.54 1
 
< 0.1%
32.55 3
 
< 0.1%
32.56 10
 
< 0.1%
32.57 18
0.1%
32.58 26
0.1%
32.59 11
0.1%
32.6 9
 
< 0.1%
32.61 14
0.1%
32.62 13
0.1%
32.63 18
0.1%
ValueCountFrequency (%)
41.95 2
< 0.1%
41.92 1
 
< 0.1%
41.88 1
 
< 0.1%
41.86 3
< 0.1%
41.84 1
 
< 0.1%
41.82 1
 
< 0.1%
41.81 2
< 0.1%
41.8 3
< 0.1%
41.79 1
 
< 0.1%
41.78 3
< 0.1%

housing_median_age
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.639486
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2023-08-23T12:37:04.425892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median29
Q337
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.585558
Coefficient of variation (CV)0.43944774
Kurtosis-0.80062885
Mean28.639486
Median Absolute Deviation (MAD)10
Skewness0.060330638
Sum591119
Variance158.39626
MonotonicityNot monotonic
2023-08-23T12:37:04.604208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 1273
 
6.2%
36 862
 
4.2%
35 824
 
4.0%
16 771
 
3.7%
17 698
 
3.4%
34 689
 
3.3%
26 619
 
3.0%
33 615
 
3.0%
18 570
 
2.8%
25 566
 
2.7%
Other values (42) 13153
63.7%
ValueCountFrequency (%)
1 4
 
< 0.1%
2 58
 
0.3%
3 62
 
0.3%
4 191
0.9%
5 244
1.2%
6 160
0.8%
7 175
0.8%
8 206
1.0%
9 205
1.0%
10 264
1.3%
ValueCountFrequency (%)
52 1273
6.2%
51 48
 
0.2%
50 136
 
0.7%
49 134
 
0.6%
48 177
 
0.9%
47 198
 
1.0%
46 245
 
1.2%
45 294
 
1.4%
44 356
 
1.7%
43 353
 
1.7%

total_rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct5926
Distinct (%)28.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2635.7631
Minimum2
Maximum39320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2023-08-23T12:37:04.778124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile620.95
Q11447.75
median2127
Q33148
95-th percentile6213.2
Maximum39320
Range39318
Interquartile range (IQR)1700.25

Descriptive statistics

Standard deviation2181.6153
Coefficient of variation (CV)0.82769778
Kurtosis32.630927
Mean2635.7631
Median Absolute Deviation (MAD)797
Skewness4.1473435
Sum54402150
Variance4759445.1
MonotonicityNot monotonic
2023-08-23T12:37:04.964204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1527 18
 
0.1%
1613 17
 
0.1%
1582 17
 
0.1%
2127 16
 
0.1%
1717 15
 
0.1%
2053 15
 
0.1%
1607 15
 
0.1%
1722 15
 
0.1%
1471 15
 
0.1%
1703 15
 
0.1%
Other values (5916) 20482
99.2%
ValueCountFrequency (%)
2 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
11 1
 
< 0.1%
12 1
 
< 0.1%
15 2
< 0.1%
16 1
 
< 0.1%
18 4
< 0.1%
19 2
< 0.1%
20 2
< 0.1%
ValueCountFrequency (%)
39320 1
< 0.1%
37937 1
< 0.1%
32627 1
< 0.1%
32054 1
< 0.1%
30450 1
< 0.1%
30405 1
< 0.1%
30401 1
< 0.1%
28258 1
< 0.1%
27870 1
< 0.1%
27700 1
< 0.1%

total_bedrooms
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1923
Distinct (%)9.4%
Missing207
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean537.87055
Minimum1
Maximum6445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2023-08-23T12:37:05.144814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile137
Q1296
median435
Q3647
95-th percentile1275.4
Maximum6445
Range6444
Interquartile range (IQR)351

Descriptive statistics

Standard deviation421.38507
Coefficient of variation (CV)0.78343213
Kurtosis21.985575
Mean537.87055
Median Absolute Deviation (MAD)162
Skewness3.4595463
Sum10990309
Variance177565.38
MonotonicityNot monotonic
2023-08-23T12:37:05.316115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
280 55
 
0.3%
331 51
 
0.2%
345 50
 
0.2%
343 49
 
0.2%
393 49
 
0.2%
348 48
 
0.2%
394 48
 
0.2%
328 48
 
0.2%
309 47
 
0.2%
272 47
 
0.2%
Other values (1913) 19941
96.6%
(Missing) 207
 
1.0%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 5
< 0.1%
4 7
< 0.1%
5 6
< 0.1%
6 5
< 0.1%
7 6
< 0.1%
8 8
< 0.1%
9 7
< 0.1%
10 8
< 0.1%
ValueCountFrequency (%)
6445 1
< 0.1%
6210 1
< 0.1%
5471 1
< 0.1%
5419 1
< 0.1%
5290 1
< 0.1%
5033 1
< 0.1%
5027 1
< 0.1%
4957 1
< 0.1%
4952 1
< 0.1%
4819 1
< 0.1%

population
Real number (ℝ)

HIGH CORRELATION 

Distinct3888
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1425.4767
Minimum3
Maximum35682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2023-08-23T12:37:05.489868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile348
Q1787
median1166
Q31725
95-th percentile3288
Maximum35682
Range35679
Interquartile range (IQR)938

Descriptive statistics

Standard deviation1132.4621
Coefficient of variation (CV)0.79444447
Kurtosis73.553116
Mean1425.4767
Median Absolute Deviation (MAD)440
Skewness4.9358582
Sum29421840
Variance1282470.5
MonotonicityNot monotonic
2023-08-23T12:37:05.667478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
891 25
 
0.1%
761 24
 
0.1%
1227 24
 
0.1%
1052 24
 
0.1%
850 24
 
0.1%
825 23
 
0.1%
782 22
 
0.1%
999 22
 
0.1%
1005 22
 
0.1%
753 21
 
0.1%
Other values (3878) 20409
98.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
8 4
< 0.1%
9 2
< 0.1%
11 1
 
< 0.1%
13 4
< 0.1%
14 3
< 0.1%
15 2
< 0.1%
17 2
< 0.1%
ValueCountFrequency (%)
35682 1
< 0.1%
28566 1
< 0.1%
16305 1
< 0.1%
16122 1
< 0.1%
15507 1
< 0.1%
15037 1
< 0.1%
13251 1
< 0.1%
12873 1
< 0.1%
12427 1
< 0.1%
12203 1
< 0.1%

households
Real number (ℝ)

HIGH CORRELATION 

Distinct1815
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean499.53968
Minimum1
Maximum6082
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2023-08-23T12:37:05.842690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile125
Q1280
median409
Q3605
95-th percentile1162
Maximum6082
Range6081
Interquartile range (IQR)325

Descriptive statistics

Standard deviation382.32975
Coefficient of variation (CV)0.76536413
Kurtosis22.057988
Mean499.53968
Median Absolute Deviation (MAD)151
Skewness3.4104377
Sum10310499
Variance146176.04
MonotonicityNot monotonic
2023-08-23T12:37:06.018672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 57
 
0.3%
386 56
 
0.3%
335 56
 
0.3%
282 55
 
0.3%
429 54
 
0.3%
375 53
 
0.3%
284 51
 
0.2%
297 51
 
0.2%
278 50
 
0.2%
340 50
 
0.2%
Other values (1805) 20107
97.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
4 4
 
< 0.1%
5 7
< 0.1%
6 5
< 0.1%
7 10
< 0.1%
8 8
< 0.1%
9 9
< 0.1%
10 7
< 0.1%
ValueCountFrequency (%)
6082 1
< 0.1%
5358 1
< 0.1%
5189 1
< 0.1%
5050 1
< 0.1%
4930 1
< 0.1%
4855 1
< 0.1%
4769 1
< 0.1%
4616 1
< 0.1%
4490 1
< 0.1%
4372 1
< 0.1%

median_income
Real number (ℝ)

HIGH CORRELATION 

Distinct12928
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.870671
Minimum0.4999
Maximum15.0001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2023-08-23T12:37:06.188578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.60057
Q12.5634
median3.5348
Q34.74325
95-th percentile7.300305
Maximum15.0001
Range14.5002
Interquartile range (IQR)2.17985

Descriptive statistics

Standard deviation1.8998217
Coefficient of variation (CV)0.4908249
Kurtosis4.9525241
Mean3.870671
Median Absolute Deviation (MAD)1.0642
Skewness1.6466567
Sum79890.649
Variance3.6093226
MonotonicityNot monotonic
2023-08-23T12:37:06.368624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.125 49
 
0.2%
15.0001 49
 
0.2%
2.875 46
 
0.2%
2.625 44
 
0.2%
4.125 44
 
0.2%
3.875 41
 
0.2%
3.375 38
 
0.2%
3 38
 
0.2%
4 37
 
0.2%
3.625 37
 
0.2%
Other values (12918) 20217
98.0%
ValueCountFrequency (%)
0.4999 12
0.1%
0.536 10
< 0.1%
0.5495 1
 
< 0.1%
0.6433 1
 
< 0.1%
0.6775 1
 
< 0.1%
0.6825 1
 
< 0.1%
0.6831 1
 
< 0.1%
0.696 1
 
< 0.1%
0.6991 1
 
< 0.1%
0.7007 1
 
< 0.1%
ValueCountFrequency (%)
15.0001 49
0.2%
15 2
 
< 0.1%
14.9009 1
 
< 0.1%
14.5833 1
 
< 0.1%
14.4219 1
 
< 0.1%
14.4113 1
 
< 0.1%
14.2959 1
 
< 0.1%
14.2867 1
 
< 0.1%
13.947 1
 
< 0.1%
13.8556 1
 
< 0.1%

median_house_value
Real number (ℝ)

HIGH CORRELATION 

Distinct3842
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean206855.82
Minimum14999
Maximum500001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size161.4 KiB
2023-08-23T12:37:06.547664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14999
5-th percentile66200
Q1119600
median179700
Q3264725
95-th percentile489810
Maximum500001
Range485002
Interquartile range (IQR)145125

Descriptive statistics

Standard deviation115395.62
Coefficient of variation (CV)0.55785531
Kurtosis0.32787024
Mean206855.82
Median Absolute Deviation (MAD)68400
Skewness0.97776327
Sum4.2695041 × 109
Variance1.3316148 × 1010
MonotonicityNot monotonic
2023-08-23T12:37:06.725556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500001 965
 
4.7%
137500 122
 
0.6%
162500 117
 
0.6%
112500 103
 
0.5%
187500 93
 
0.5%
225000 92
 
0.4%
350000 79
 
0.4%
87500 78
 
0.4%
275000 65
 
0.3%
150000 64
 
0.3%
Other values (3832) 18862
91.4%
ValueCountFrequency (%)
14999 4
< 0.1%
17500 1
 
< 0.1%
22500 4
< 0.1%
25000 1
 
< 0.1%
26600 1
 
< 0.1%
26900 1
 
< 0.1%
27500 1
 
< 0.1%
28300 1
 
< 0.1%
30000 2
< 0.1%
32500 4
< 0.1%
ValueCountFrequency (%)
500001 965
4.7%
500000 27
 
0.1%
499100 1
 
< 0.1%
499000 1
 
< 0.1%
498800 1
 
< 0.1%
498700 1
 
< 0.1%
498600 1
 
< 0.1%
498400 1
 
< 0.1%
497600 1
 
< 0.1%
497400 1
 
< 0.1%

ocean_proximity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size161.4 KiB
<1H OCEAN
9136 
INLAND
6551 
NEAR OCEAN
2658 
NEAR BAY
2290 
ISLAND
 
5

Length

Max length10
Median length9
Mean length8.0649225
Min length6

Characters and Unicode

Total characters166460
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNEAR BAY
2nd rowNEAR BAY
3rd rowNEAR BAY
4th rowNEAR BAY
5th rowNEAR BAY

Common Values

ValueCountFrequency (%)
<1H OCEAN 9136
44.3%
INLAND 6551
31.7%
NEAR OCEAN 2658
 
12.9%
NEAR BAY 2290
 
11.1%
ISLAND 5
 
< 0.1%

Length

2023-08-23T12:37:07.161652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-23T12:37:07.317766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
ocean 11794
34.0%
1h 9136
26.3%
inland 6551
18.9%
near 4948
14.2%
bay 2290
 
6.6%
island 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 29849
17.9%
A 25588
15.4%
E 16742
10.1%
14084
8.5%
O 11794
 
7.1%
C 11794
 
7.1%
< 9136
 
5.5%
1 9136
 
5.5%
H 9136
 
5.5%
I 6556
 
3.9%
Other values (6) 22645
13.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 134104
80.6%
Space Separator 14084
 
8.5%
Math Symbol 9136
 
5.5%
Decimal Number 9136
 
5.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 29849
22.3%
A 25588
19.1%
E 16742
12.5%
O 11794
 
8.8%
C 11794
 
8.8%
H 9136
 
6.8%
I 6556
 
4.9%
L 6556
 
4.9%
D 6556
 
4.9%
R 4948
 
3.7%
Other values (3) 4585
 
3.4%
Space Separator
ValueCountFrequency (%)
14084
100.0%
Math Symbol
ValueCountFrequency (%)
< 9136
100.0%
Decimal Number
ValueCountFrequency (%)
1 9136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 134104
80.6%
Common 32356
 
19.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 29849
22.3%
A 25588
19.1%
E 16742
12.5%
O 11794
 
8.8%
C 11794
 
8.8%
H 9136
 
6.8%
I 6556
 
4.9%
L 6556
 
4.9%
D 6556
 
4.9%
R 4948
 
3.7%
Other values (3) 4585
 
3.4%
Common
ValueCountFrequency (%)
14084
43.5%
< 9136
28.2%
1 9136
28.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 29849
17.9%
A 25588
15.4%
E 16742
10.1%
14084
8.5%
O 11794
 
7.1%
C 11794
 
7.1%
< 9136
 
5.5%
1 9136
 
5.5%
H 9136
 
5.5%
I 6556
 
3.9%
Other values (6) 22645
13.6%

Interactions

2023-08-23T12:37:01.694345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:50.603670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:51.769930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:52.958137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:54.136081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:55.319423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:56.585508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:57.778486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:59.233759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:00.451113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:01.809430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:50.717542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:51.878948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:53.080499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:54.245700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:55.435372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:56.697315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:57.891819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:59.345067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:00.567666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:01.926438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:50.831456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:51.993453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:53.203749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:54.361129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:55.561484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:56.814877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:58.016610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:59.463006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:00.688724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:02.038918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:50.941901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:52.103981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:53.313083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:54.471576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:55.682808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:56.926924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:58.139923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:59.576496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:00.807116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:02.154107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:51.059463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:52.220697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:53.429236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:54.590314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:55.806969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:57.043731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:58.268110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:59.697006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:00.925580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:02.284304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:51.185921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:52.347815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:53.554484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:54.718386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:55.941987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:57.175726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:58.616469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:59.830684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:01.064846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:02.403527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:51.301482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:52.468473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:53.670870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:54.837979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:56.070820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:57.294107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:58.736938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:59.950135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:01.192062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:02.529787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:51.421165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:52.592306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:53.790355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:54.961379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:56.202725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:57.418410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:58.860862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:00.081996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:01.322817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:02.651924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:51.537745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:52.711509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:53.905668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:55.080758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:56.331234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:57.537313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:58.983728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:00.202903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:01.448097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:02.773995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:51.656601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:52.836437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:54.024211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:55.204186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:56.462699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:57.662256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:36:59.110466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:00.331106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-23T12:37:01.576532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-23T12:37:07.437119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
df_indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
df_index1.000-0.0390.034-0.1880.0900.0490.0300.0510.0960.0770.439
longitude-0.0391.000-0.879-0.1510.0400.0640.1240.060-0.010-0.0700.425
latitude0.034-0.8791.0000.032-0.018-0.057-0.124-0.074-0.088-0.1660.470
housing_median_age-0.188-0.1510.0321.000-0.357-0.307-0.284-0.282-0.1470.0750.190
total_rooms0.0900.040-0.018-0.3571.0000.9150.8160.9070.2710.2060.021
total_bedrooms0.0490.064-0.057-0.3070.9151.0000.8710.976-0.0060.0860.017
population0.0300.124-0.124-0.2840.8160.8711.0000.9040.0060.0040.014
households0.0510.060-0.074-0.2820.9070.9760.9041.0000.0300.1130.019
median_income0.096-0.010-0.088-0.1470.271-0.0060.0060.0301.0000.6770.125
median_house_value0.077-0.070-0.1660.0750.2060.0860.0040.1130.6771.0000.302
ocean_proximity0.4390.4250.4700.1900.0210.0170.0140.0190.1250.3021.000

Missing values

2023-08-23T12:37:02.941428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-23T12:37:03.158168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

df_indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
00-122.2337.8841.0880.0129.0322.0126.08.3252452600.0NEAR BAY
11-122.2237.8621.07099.01106.02401.01138.08.3014358500.0NEAR BAY
22-122.2437.8552.01467.0190.0496.0177.07.2574352100.0NEAR BAY
33-122.2537.8552.01274.0235.0558.0219.05.6431341300.0NEAR BAY
44-122.2537.8552.01627.0280.0565.0259.03.8462342200.0NEAR BAY
55-122.2537.8552.0919.0213.0413.0193.04.0368269700.0NEAR BAY
66-122.2537.8452.02535.0489.01094.0514.03.6591299200.0NEAR BAY
77-122.2537.8452.03104.0687.01157.0647.03.1200241400.0NEAR BAY
88-122.2637.8442.02555.0665.01206.0595.02.0804226700.0NEAR BAY
99-122.2537.8452.03549.0707.01551.0714.03.6912261100.0NEAR BAY
df_indexlongitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_valueocean_proximity
2063020630-121.3239.2911.02640.0505.01257.0445.03.5673112000.0INLAND
2063120631-121.4039.3315.02655.0493.01200.0432.03.5179107200.0INLAND
2063220632-121.4539.2615.02319.0416.01047.0385.03.1250115600.0INLAND
2063320633-121.5339.1927.02080.0412.01082.0382.02.549598300.0INLAND
2063420634-121.5639.2728.02332.0395.01041.0344.03.7125116800.0INLAND
2063520635-121.0939.4825.01665.0374.0845.0330.01.560378100.0INLAND
2063620636-121.2139.4918.0697.0150.0356.0114.02.556877100.0INLAND
2063720637-121.2239.4317.02254.0485.01007.0433.01.700092300.0INLAND
2063820638-121.3239.4318.01860.0409.0741.0349.01.867284700.0INLAND
2063920639-121.2439.3716.02785.0616.01387.0530.02.388689400.0INLAND